Better methods to understand how blood pressure treatment affects heart disease risk
Bayesian machine learning for causal inference with incomplete longitudinal covariates and censored survival outcomes
This project builds smarter statistical tools that use combined long-term heart study data to learn how blood pressure and antihypertensive treatment influence heart disease risk in adults.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rutgers Biomedical and Health Sciences NIH-funded |
| Lab location | 1 site (Newark, UNITED STATES) |
| Project ID | NIH-11325015 on NIH RePORTER |
What this research studies
From a patient perspective, researchers are creating new Bayesian machine-learning methods to combine data from large heart studies like ARIC and MESA while handling missing measurements and different visit schedules. These methods aim to untangle how blood pressure levels and starting antihypertensive medications affect long-term heart outcomes, even when some data are incomplete or people drop out. By pooling many cohorts and improving how uncertainties are handled, the team hopes to produce more reliable estimates that could inform treatment thresholds and prevention strategies for adults.
Who could benefit from this research
Good fit: Adults aged 21 and older concerned about blood pressure and heart disease, especially those who have taken or may take antihypertensive medications or who have participated in long-term cardiovascular cohort studies, would be most relevant to this work.
Not a fit: Children and people with conditions not related to blood pressure or who are not represented in the pooled cohorts may not directly benefit from these analyses.
Why it matters
Potential benefit: If successful, this work could lead to clearer blood pressure treatment thresholds and more personalized strategies to reduce heart disease risk.
How similar studies have performed: Previous pooled-cohort and statistical analyses have informed blood pressure guidelines but often struggle with missing data and misaligned visit times, so this project builds newer Bayesian tools to address those known gaps.
Where this research is happening
Newark, UNITED STATES
- Rutgers Biomedical and Health Sciences — Newark, United States (Active)
Researchers
- Principal investigator: Hu, Liangyuan — Rutgers Biomedical and Health Sciences
- Study coordinator: Hu, Liangyuan
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.